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In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations where the fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known.
As one of the leading experts in econometrics, her research focuses on econometric theory, Semi/nonparametric estimation and inference methods, Sieve methods, Nonlinear time series, and Semi/nonparametric models. [2] She was elected to the American Academy of Arts and Sciences in 2019. [3]
It may appear at first that semiparametric models include nonparametric models, since they have an infinite-dimensional as well as a finite-dimensional component. However, a semiparametric model is considered to be "smaller" than a completely nonparametric model because we are often interested only in the finite-dimensional component of θ ...
They also applied the smoothing spline technique for their research. There was a case of application of partially linear model in biometrics by Zeger and Diggle in 1994. The research objective of their paper is the evolution period cycle of CD4 cell amounts in HIV (Human immune-deficiency virus) seroconverters (Zeger and Diggle, 1994). [3]
Broadly speaking, there are two classes of predictive models: parametric and non-parametric. A third class, semi-parametric models, includes features of both. Parametric models make "specific assumptions with regard to one or more of the population parameters that characterize the underlying distribution(s)". [3]
Parametric models are contrasted with the semi-parametric, semi-nonparametric, and non-parametric models, all of which consist of an infinite set of "parameters" for description. The distinction between these four classes is as follows: [citation needed] in a "parametric" model all the parameters are in finite-dimensional parameter spaces;
Survival analysis is normally carried out using parametric models, semi-parametric models, non-parametric models to estimate the survival rate in clinical research. However recently Bayesian models [1] are also used to estimate the survival rate due to their ability to handle design and analysis issues in clinical research.
non-parametric regression, which is modeling whereby the structure of the relationship between variables is treated non-parametrically, but where nevertheless there may be parametric assumptions about the distribution of model residuals. non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the ...